Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Muh. Rizieq Fazlulrahman Djafar
Institut Teknologi Telkom Purwokerto, Purwokerto
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Perbandingan Metode Klasifikasi Untuk Deteksi Stress Pada Mahasiswa di Perguruan Tinggi Merlinda Wibowo; Muh. Rizieq Fazlulrahman Djafar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5182

Abstract

The outbreak of the COVID-19 pandemic is increasingly affecting the high level of stress in humans. Stress due to this pandemic has also occurred, especially for students. This stress is caused by students spending too much time studying online. Using student data can act as a tool to identify student stress by processing it through various machine-learning methods. This method can extract information and find patterns and information from the data. Classification techniques are used as data groupings based on mapping data into sample data. This study used several classification methods: Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Neural Network, Random Tree, Random Forest, and K Nearest Neighbor (KNN). These methods were successfully compared to determine which is the best for detecting stress precisely and accurately based on the classification performance results of each method. Random Tree and Decision tree were chosen as the best methods for the results of this performance comparison with an 80:20 split reaching up to 100%.
Co-Authors Merlinda Wibowo